Results 1  10
of
593
The Quickhull algorithm for convex hulls
 ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE
, 1996
"... The convex hull of a set of points is the smallest convex set that contains the points. This article presents a practical convex hull algorithm that combines the twodimensional Quickhull Algorithm with the generaldimension BeneathBeyond Algorithm. It is similar to the randomized, incremental algo ..."
Abstract

Cited by 711 (0 self)
 Add to MetaCart
The convex hull of a set of points is the smallest convex set that contains the points. This article presents a practical convex hull algorithm that combines the twodimensional Quickhull Algorithm with the generaldimension BeneathBeyond Algorithm. It is similar to the randomized, incremental
The QuickHull algorithm in
"... > (right) of the line. In particular, for S upper we have S = S upper [ fu; vg with p 1 = u and p 2 = v; for S lower we set S = S lower [ fv; ug with p 1 = v and p 2 = u. Now we apply the following recursive method to S and (p 1 ; p 2 ): We determine the point pivot 2 S (called the pivot point) ..."
Abstract
 Add to MetaCart
> (right) of the line. In particular, for S upper we have S = S upper [ fu; vg with p 1 = u and p 2 = v; for S lower we set S = S lower [ fv; ug with p 1 = v and p 2 = u. Now we apply the following recursive method to S and (p 1 ; p 2 ): We determine the point pivot 2 S (called the pivot point) with the largest distance from line (p 1 ; p 2 ) (see Figure 2, left hand side), i.e. which maximizes the cross product #define cross(pivot,p1,p2) " ((x[p1]x[pivot])*(y[p2]y[pivot])  (y[p1]y[pivot])*(x[p2]x[pivot])) Obviously, pivot belongs to the conve
Finding Convex Hulls Using Quickhull on the GPU
"... We present a convex hull algorithm that is accelerated on commodity graphics hardware. We analyze and identify the hurdles of writing a recursive divide and conquer algorithm on the GPU and divise a framework for representing this class of problems. Our framework transforms the recursive splitting s ..."
Abstract
 Add to MetaCart
step into a permutation step that is wellsuited for graphics hardware. Our convex hull algorithm of choice is Quickhull. Our parallel Quickhull implementation (for both 2D and 3D cases) achieves an order of magnitude speedup over standard computational geometry libraries. 1
AN ALGORITHM TO BUILD CONVEX HULLS FOR 3D OBJECTS
"... In this paper, a new algorithm based on the Quickhull algorithm is proposed to find convex hulls for 3D objects using neighbor trees. The neighbor tree is the data structure by which all visible facets to the selected furthest outer point can be found. The neighboring sequence of ridges on the oute ..."
Abstract
 Add to MetaCart
In this paper, a new algorithm based on the Quickhull algorithm is proposed to find convex hulls for 3D objects using neighbor trees. The neighbor tree is the data structure by which all visible facets to the selected furthest outer point can be found. The neighboring sequence of ridges
Robust Classification for Imprecise Environments
, 1989
"... In realworld environments it is usually difficult to specify target operating conditions precisely. This uncertainty makes building robust classification systems problematic. We present a method for the comparison of classifier performance that is robust to imprecise class distributions and misclas ..."
Abstract

Cited by 332 (15 self)
 Add to MetaCart
In realworld environments it is usually difficult to specify target operating conditions precisely. This uncertainty makes building robust classification systems problematic. We present a method for the comparison of classifier performance that is robust to imprecise class distributions and misclassification costs. The ROC convex hull method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers. The method is efficient and incremental, minimizes the management of classifier performance data, and allows for clear visual comparisons and sensitivity analyses. We then show that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions. This robust performance extends across a wide variety of comparison frameworks, including the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and ...
Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions
 In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining
, 1997
"... Applications of inductive learning algorithms to realworld data mining problems have shown repeatedly that using accuracy to compare classifiers is not adequate because the underlying assumptions rarely hold. We present a method for the comparison of classifier performance that is robust to imprecis ..."
Abstract

Cited by 306 (15 self)
 Add to MetaCart
Applications of inductive learning algorithms to realworld data mining problems have shown repeatedly that using accuracy to compare classifiers is not adequate because the underlying assumptions rarely hold. We present a method for the comparison of classifier performance that is robust
Convex Envelope Generation Using a Mix of Gift Wrap and QuickHull Algorithms
"... The environment simulation is widely used nowadays. Training in many fields such as medicine and architecture heavily depends on virtual reality techniques. Since objects in real life do not have a deterministic shape it is not possible to have a geometric equation that might model them. Convex Hull ..."
Abstract
 Add to MetaCart
and compared. The first three algorithms are the Brute Force, the Gift Wrap and the QuickHull algorithm. The fourth one is a hybrid approach that combines the QuickHull and the Gift Wrap algorithms. Simulations were done in the medical environment, and algorithms are tested with the model of 3D wrist and knee
An Associative Implementation of Classical Convex Hull Algorithms
 Proceedings of Eighth IASTED International Conference on Parallel and Distributed Computing and Systems
, 1996
"... This paper will present the implementation and comparison of new parallel algorithms for the convex hull problem. These algorithms are a parallel adaptation of the Jarvis March and the Quickhull algorithms. The computational model selected for these algorithms is the associative computing model #ASC ..."
Abstract

Cited by 16 (5 self)
 Add to MetaCart
This paper will present the implementation and comparison of new parallel algorithms for the convex hull problem. These algorithms are a parallel adaptation of the Jarvis March and the Quickhull algorithms. The computational model selected for these algorithms is the associative computing model
A Framework for MultiCore Implementations of Divide and Conquer Algorithms and its Application to the Convex Hull Problem ∗
"... We present a framework for multicore implementations of divide and conquer algorithms and show its efficiency and ease of use by applying it to the fundamental geometric problem of computing the convex hull of a point set. We concentrate on the Quickhull algorithm introduced in [2]. In general the ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
We present a framework for multicore implementations of divide and conquer algorithms and show its efficiency and ease of use by applying it to the fundamental geometric problem of computing the convex hull of a point set. We concentrate on the Quickhull algorithm introduced in [2]. In general
Parallelizing Two Dimensional Convex Hull on NVIDIA GPU and Cell BE
"... Multicore processors are a shift of paradigm in computer architecture that promises dramatic increase in performance. But they also bring complexity in algorithmic design. In this paper we describe the challenges and design issues involved in parallelizing two dimensional convex hull on both CUDA an ..."
Abstract
 Add to MetaCart
and Cell Brodband Engine (Cell BE). We have parallelized the quickhull algorithm for two dimensional convex hull. The major advantage of this algorithm is that interprocessor communication cost is highly reduced. 1.
Results 1  10
of
593